Dive Brief:
- Agent-led software development is fundamentally changing engineering, but some tech teams are missing out on most of the productivity AI can provide, a Bain & Company report released Thursday found. The report was based on surveys of global executives and market research conducted in 2025 and 2026 by the management consulting firm.
- By the fall, respondents said they expect more than half of their engineering efforts to be agent-assisted, and by spring 2027, that number rose to about 90%. While respondents reported AI is producing 63% higher outputs per engineer, most companies said they’re not yet seeing large enough increases in their overall efficiency as they figure out new roles in managing AI output.
- For value to be derived, tech teams need to make adjustments to all levels of their engineering operations when introducing AI agents, not by plugging in agents into singular activities, such as code generation or testing, said Purna Doddapaneni, a partner at Bain & Company and coauthor of the report. “In an agentic world, agents are doing the work,” he said. “And as humans or organizations, we are responsible for the decisions that the agents are making.”
Dive Insight:
Agents are no longer just making developers incrementally faster, they’re rebuilding the entire delivery system around engineering, Doddapaneni said.
Adopting AI into engineering workflows has become the default, The State of Engineering Excellence 2026 report, published by software platform Harness, found last month. The technology has changed the way engineering teams complete work and measure productivity, with more time spent on reviewing code, fixing bugs and context switching between tools.
But teams that fail to adjust their operations around advancing tools won’t gain the benefits of them, Doddapaneni said. The report recommended humans should act as strategic advisers and incident responders through the AI-assisted workflows. Product and engineering teams will likely adopt hybrid agentic models, where developers can move from being code writers to agent orchestrators.
More powerful AI models don’t derive value without the processes put in place to direct them toward a company’s goals and objectives, Doddapaneni said.
“A model is like an engine, just having a beefier passenger engine doesn’t really help me drive the car — I need to think about the steering wheel, the brakes and everything around it,” he said. “It’s like I’m building a harness around the engine, or the model, giving it context to get me from one place to the other.”
Most engineering processes include both a product development life cycle — which defines what to build based on customer needs, market opportunity and strategic roadmap — and a software development life cycle, which is the process of building the product through coding, testing and deploying.
The sequential nature of development is disappearing in the AI era, Doddapaneni said, as humans spend less time developing products and writing code, and instead spend their time managing AI outputs. He called it “technical scaffolding,” or putting the guardrails and guidelines in place for AI to create the desired outcomes.
“CIOs who are still running pilots without that system redesign are going to hit the ceiling,” he said.
